Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns

Research output: Contribution to journalArticleScientificpeer-review

Researchers

  • Marcin J. Skwark
  • Daniele Raimondi
  • Mirco Michel
  • Arne Elofsson

Research units

Abstract

Given sufficient large protein families, and using a global statistical inference approach, it is possible to obtain sufficient accuracy in protein residue contact predictions to predict the structure of many proteins. However, these approaches do not consider the fact that the contacts in a protein are neither randomly, nor independently distributed, but actually follow precise rules governed by the structure of the protein and thus are interdependent. Here, we present PconsC2, a novel method that uses a deep learning approach to identify protein-like contact patterns to improve contact predictions. A substantial enhancement can be seen for all contacts independently on the number of aligned sequences, residue separation or secondary structure type, but is largest for β-sheet containing proteins. In addition to being superior to earlier methods based on statistical inferences, in comparison to state of the art methods using machine learning, PconsC2 is superior for families with more than 100 effective sequence homologs. The improved contact prediction enables improved structure prediction.

Details

Original languageEnglish
Article numbere1003889
Pages (from-to)1-14
JournalPLoS computational biology
Volume10
Issue number11
Publication statusPublished - 2014
MoE publication typeA1 Journal article-refereed

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